Bone morphogenetic protein 2 upregulates SERPINE2 expression through noncanonical SMAD2/3 and p38 MAPK signaling pathways in human granulosa‐lutein cells
Bibliographic record
Abstract
Serine protease inhibitor-E2 (SERPINE2) is highly expressed in the granulosa cells of growing follicles and the dynamic changes in SERPINE2 expression are correlated with follicular development and ovulation in several mammals, including mice, cattle, sheep, and humans. Bone morphogenetic proteins (BMPs) and their functional receptors are extensively expressed in the ovary and play critical roles in the regulation of ovarian folliculogenesis and luteal function. To date, whether BMPs regulate the expression of SERPINE2 during human follicular development remains to be elucidated. The aim of this study was to investigate the effects of BMPs on the regulation of SERPINE2 expression (a major regulator of plasminogen activators [PA]) and the underlying mechanisms using primary and immortalized human granulosa-lutein (hGL) cells. Our results demonstrated that these BMPs (BMP2, BMP4, BMP6, BMP7, and BMP15) induced differential upregulation of SERPINE2 expression. In this regard, BMP2 is the major modulator that has the best cellular activity, which further decreased the production of urokinase PA and tissue PA in hGL cells. In addition to canonical SMAD1/5/8 signaling, BMP2 also activates noncanonical SMAD2/3 and p38 mitogen-activated protein kinase (MAPK) signaling. Using two inhibition approaches (kinase receptor inhibitors and siRNA-mediated knockdown), we found that SMAD2/3-SMAD4 and p38 MAPK, but not SMAD1/5/8 signaling, was involved in the BMP2-induced upregulation of SERPINE2 expression via activin receptor-like kinase 3. These findings deepen our understanding of the differential effect of BMPs in regulating follicular function and provide new insights of the molecular mechanisms by which BMP2 regulates the expression of SERPINE2 in human granulosa cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".